[R] FW: warnings in lmer estimation after mice
Raquel Guimaraes
raquelrguima at gmail.com
Sat Jun 30 05:02:37 CEST 2012
Hi all,
I need help to understand convergence problems with my estimation.
I am using lmer to estimate a mixed-effects model of an index of teacher
quality on student achievement gains.
The dataset for female students is available in the link above and contains
original and imputed data (m=5 and m=50)
<https://www.dropbox.com/sh/fqpjtkkuz9y9mzr/MhrMXwlrFp>
I want to understand the error messages above and their implications for my
parameter estimates.
1: In mer_finalize(ans) : false convergence (8)
2: In mer_finalize(ans) : singular convergence (7)
3: In mer_finalize(ans) : singular convergence (7)
4: In mer_finalize(ans) : false convergence (8)
5: In mer_finalize(ans) : false convergence (8)
Below you may find the script for my procedures.
Any help will be appreciated!
Thanks,
Raquel
-----------------------------------------------------------------
#Multiple imputation
library("mice")
md.pattern(data.f)
md.pairs(data.f)
# 5 imputations - Predictive mean matching
imp.5.f<-mice(data.f,m=5,seed=23109)
print(imp.5.f)
# 50 imputations - Predictive mean matching
imp.50.f<-mice(data.f,m=50,seed=23109)
print(imp.50.f)
#Running lmer
library(lme4)
#Imputed data
#Females
Uncpde.math.f.mi <- with(imp.50.f, lmer(gain ~ index +
( index | std_id), verbose=TRUE))
print(pool(Uncpde.math.f.mi))
round(summary(pool(Uncpde.math.f.mi)), 2)
> Uncpde.math.f.mi <- with(imp.50.f, lmer(gain ~ index +
+ ( index | std_id), verbose=TRUE))
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13: 87941.366: 0.0921629 2.75824e-006 -0.164807
14: 87941.365: 0.0918365 0.000000 -0.164754
15: 87941.365: 0.0918121 1.05293e-009 -0.164776
16: 87941.365: 0.0916407 1.25385e-007 -0.164902
17: 87941.365: 0.0916280 0.000000 -0.164885
18: 87941.365: 0.0915976 1.78900e-007 -0.164855
19: 87941.365: 0.0915710 0.000000 -0.164868
20: 87941.365: 0.0915709 6.77374e-008 -0.164868
21: 87941.365: 0.0915707 0.000000 -0.164868
22: 87941.365: 0.0915706 0.000000 -0.164869
23: 87941.365: 0.0915706 0.000000 -0.164869
0: 90538.952: 0.666667 0.0906664 0.000000
1: 90188.780: 0.580152 0.000000 -0.298077
2: 87995.424: 0.280946 0.000000 -0.0982630
3: 87965.709: 0.263221 1.10254e-009 -0.127679
4: 87950.953: 0.232057 0.00156023 -0.142027
5: 87932.225: 0.160075 0.000000 -0.123961
6: 87924.828: 0.107481 0.000000 -0.177222
7: 87923.913: 0.101794 1.32194e-009 -0.172355
8: 87922.762: 0.0898806 0.000000 -0.163290
9: 87922.603: 0.0870883 2.97543e-007 -0.162210
10: 87921.808: 0.0643480 0.000000 -0.154688
11: 87921.756: 0.0619822 1.91616e-007 -0.155062
12: 87921.461: 0.0430376 0.000000 -0.157938
13: 87921.329: 0.0246316 0.000000 -0.160343
14: 87921.290: 0.0115580 0.000000 -0.161904
15: 87921.281: 0.00286298 0.000000 -0.162911
16: 87921.281: 0.00189741 3.55755e-005 -0.162906
17: 87921.280: 0.000934638 0.000000 -0.162938
18: 87921.280: 0.000000 0.000000 -0.162968
19: 87921.280: 0.000000 0.000000 -0.162968
0: 89913.444: 0.666667 0.0906915 0.000000
1: 89490.109: 0.578190 0.000000 -0.300666
2: 87265.611: 0.273646 0.000000 -0.0992267
3: 87184.839: 0.000000 0.000000 -0.273871
4: 87184.839: 0.000000 0.000000 -0.273871
0: 91445.647: 0.666667 0.0887779 0.000000
1: 91205.368: 0.586436 0.000000 -0.295702
2: 88923.840: 0.258811 0.000000 -0.103445
3: 88907.893: 0.242519 0.000000 -0.128815
4: 88901.918: 0.218662 0.000000 -0.147252
5: 88895.042: 0.195226 0.000965137 -0.128307
6: 88890.006: 0.169408 0.000000 -0.143102
7: 88886.667: 0.140591 0.000000 -0.134234
8: 88884.703: 0.113739 4.18354e-009 -0.147949
9: 88883.819: 0.0890199 0.000000 -0.150225
10: 88883.509: 0.0702513 0.000000 -0.151673
11: 88883.409: 0.0564583 0.000000 -0.152518
12: 88883.380: 0.0469538 0.000000 -0.152913
13: 88883.374: 0.0411088 0.000000 -0.152998
14: 88883.372: 0.0382539 0.000000 -0.152898
15: 88883.372: 0.0371689 0.000000 -0.152722
16: 88883.372: 0.0365899 0.000000 -0.152428
17: 88883.372: 0.0362285 0.000000 -0.151744
18: 88883.371: 0.0366140 0.000000 -0.150915
19: 88883.371: 0.0373644 0.000000 -0.150538
20: 88883.371: 0.0373648 4.72818e-007 -0.150539
21: 88883.371: 0.0375860 0.000000 -0.150728
22: 88883.371: 0.0378454 0.000000 -0.150594
Warning messages:
1: In mer_finalize(ans) : false convergence (8)
2: In mer_finalize(ans) : singular convergence (7)
3: In mer_finalize(ans) : singular convergence (7)
4: In mer_finalize(ans) : false convergence (8)
5: In mer_finalize(ans) : false convergence (8)
> print(pool(Uncpde.math.f.mi))
Call: pool(object = Uncpde.math.f.mi)
Pooled coefficients:
(Intercept) index
1.90889735 0.02891385
Fraction of information about the coefficients missing due to nonresponse:
(Intercept) index
0.9464698 0.9361655
> round(summary(pool(Uncpde.math.f.mi)), 2)
est se t df Pr(>|t|) lo 95 hi 95 nmis fmi lambda
(Intercept) 1.91 0.27 6.98 49.87 0.0 1.36 2.46 NA 0.95 0.94
index 0.03 0.03 0.85 51.68 0.4 -0.04 0.10 11776 0.94 0.93
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